Grant Supports New Interdisciplinary PhD Program Focused on Big Data and Urbanization

Virginia Tech will create a new Ph.D. certificate program focused on big data and urbanization with a grant of nearly $3 million over a five-year period from the National Science Foundation Research Traineeship Program.

The urban computing certificate program will launch at Virginia Tech in the spring 2016 semester. The university will recruit a diverse cadre of students from both the Blacksburg and National Capital Region campuses who are pursuing a Ph.D. in one of eight departments: computer science, mathematics, statistics, electrical and computer engineering, population health sciences, urban affairs and planning, civil and environmental engineering, or sociology.

Nineteen faculty from these and other departments have collaborated on an innovative “tapestry” curriculum for urban computing that weaves interdisciplinary issues; emphasizes ethical and societal issues for responsible data science; builds community through interdisciplinary project teams and data analytics competitions; and imparts effective communication skills that help facilitate interactions with a broad range of urban city professionals who are the end consumers of data science.

Students who complete the program requirements will receive a certificate in urban computing in addition to the Ph.D. they earn in their field.

“Projections show that by the year 2030, six out of every 10 people in the world will live in a city. As cities become more wired and networked, big data methods hold great promise for addressing urban issues. This NSF grant will allow Virginia Tech to fund 18 — and train as many as 60 — doctoral students as urban computing practitioners who can contribute to the nation’s needs in this area,” said Naren Ramakrishnan, Thomas L. Phillips Professor of Engineering, director of the Discovery Analytics Center, and principal investigator on the award.

“Many urban computing problems occur on the boundaries of multiple disciplines,” said Marathe. “The certificate program will provide students with innovative education and training opportunities including interaction with diverse faculty members and participation in ongoing projects at departments and institutes.”

Ramakrishnan said that trainees will learn how to model cities, develop large-scale statistical models, and utilize data mining and visualization technologies to pose and answer questions. “We will use the National Capital Region’s urban living laboratory model to explore collaborations with regional industries, local city governments, and local health departments through internships, practicums, data challenges, and hackathons,” Ramakrishnan said.

“In funding this program, the NSF is reinforcing Virginia Tech’s strengths in interdisciplinary education and research,” said Watson.

Embree added that the graduate certificate, complemented by Virginia Tech’s new undergraduate major in Computational Modeling and Data Analytics, contributes to the university's distinctive strength in computational data science and its application to timely challenges of societal importance.

“Virginia Tech is becoming a destination for students and faculty interested in such quantitative problem solving,” said Embree.

The program will be evaluated annually using a mixed method (quantitative and qualitative) approach with five target focus areas: students, community, research, program, scalability and sustainability. Per NSF requirement, an external consultant supported by the foundation will conduct the evaluation. Co-investigator House will be responsible for data gathering from inside Virginia Tech and coordinating with the consultant. “Assessment is key for identifying how successful the program is and how it might improve over the next five years and beyond,” said House.

Karen DePauw, vice president and dean of graduate education at Virginia Tech, will chair the internal advisory board. “The certificate program will be a good addition to our existing interdisciplinary offerings. Urban computing is a compelling concept with important research issues that can be solved through the methods of data science,” said DePauw.